Development of a spatial dimension-based taxonomy for classifying the defect patterns in a wafer bin map

Abstract

A wafer bin map (WBM) represents the locational information of defective chips on the wafer. The spatial correlation of defects on the wafer provides crucial information for the root cause diagnosis of defects in wafer fabrication. The spatial correlation is classified as a defect pattern for efficient diagnostics. A defect pattern taxonomy should be defined in advance for coherent classification of defect patterns. Various taxonomies are used in previous studies, but they share common limitations in that the differentiation among defect patterns is unclear, the set of predefined defect patterns is insufficient, and they cannot accommodate newly-emerged defect patterns. A concept of spatial dimension-based defect pattern taxonomy and its development procedure are proposed. Defect patterns are defined by three spatial dimensions, namely, Shape, Size, and Location. The development procedure is applied to a major NAND flash memory semiconductor manufacturer for two years. Results show that spatial dimension-based taxonomy can improve the performance of the defect pattern classification system by alleviating common existing limitations. Moreover, meaningful defect patterns for diagnostics are retained through the engineers’ involvement in the development procedure.